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All Journal Buana Information Technology and Computer Sciences (BIT and CS) Journal of Soft Computing Exploration International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Aerospace Engineering Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Geosciences and Environmental Studies Jurnal Teknokes
Paneru, Biplov
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Aerospace Engineering Astronomy Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Journal : Buana Information Technology and Computer Sciences (BIT and CS)

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Reinforcement Learning-Based Autonomous Soccer Agents: A Study in Multi-Agent Coordination and Strategy Development Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Poudyal, Khem
Buana Information Technology and Computer Sciences (BIT and CS) Vol 6 No 1 (2025): Buana Information Technology and Computer Sciences (BIT and CS) (InProcess)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v6i1.7270

Abstract

Reinforcement learning (RL) approaches, particularly Q-learning, have emerged as strong tools for autonomous agent training, allowing agents to acquire optimum decision-making rules through interaction with their surroundings. This research investigates the use of Q-learning in the context of training autonomous agents for robotic soccer, a complex and dynamic arena that necessitates strategic planning, coordination, and adaptation. We studied the learning progress and performance of agents taught using Q-learning in a series of experiments carried out in a simulated soccer setting. During training, agents interacted with the soccer environment, iteratively changing their Q-values in response to observable rewards and behaviors. Despite the high-dimensional and stochastic character of the soccer domain, Q-learning helped the agents develop excellent tactics and decision-making capabilities. Notably, our study found that, on average, the agents required 64 steps to reach a stable policy with an average reward of -1.
Co-Authors Adhikari, Pukar Bikram Shah, Krishna Neupne, Kabita Pandey, Nitish Paneru, Bishwash Poudel, Yam Krishna Poudyal, Khem Poudyal, Khem Narayan Poudyal, Ramhari Sapkota, Sanjog Chhetri Shah, Krishna Bikram Shah, Krishna Bikram Bikram